Cleaner, stronger cement recipes designed in record time by AI

Researchers at the Paul Scherrer Institute (PSI) have developed an AI-driven approach to design low-carbon cement recipes up to 1,000 times faster than traditional methods. Cement production is a major source of CO₂ emissions, primarily due to the chemical release of CO₂ from limestone during clinker formation. To address this, the PSI team, led by mathematician Romana Boiger, combined thermodynamic modeling software (GEMS) with experimental data to train a neural network that rapidly predicts the mineral composition and mechanical properties of various cement formulations. This AI model enables quick simulation and optimization of cement recipes that reduce carbon emissions while maintaining strength and quality. Beyond speeding up calculations, the researchers employed genetic algorithms to identify optimal cement compositions that balance CO₂ reduction with practical production feasibility. While these AI-designed formulations show promise, extensive laboratory testing and validation remain necessary before widespread adoption. This study serves as a proof of concept, demonstrating that AI can revolutionize the search for sustainable building materials by efficiently navigating complex chemical